Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195828 
Year of Publication: 
2018
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-23
Publisher: 
MDPI, Basel
Abstract: 
This work addresses crucial questions about the robustness of the PSDization process for applications in insurance. PSDization refers to the process that forces a matrix to become positive semidefinite. For companies using copulas to aggregate risks in their internal model, PSDization occurs when working with correlation matrices to compute the Solvency Capital Requirement (SCR). We examine how classical operational choices concerning the modelling of risk dependence impacts the SCR during PSDization. These operations refer to the permutations of risks (or business lines) in the correlation matrix, the addition of a new risk, and the introduction of confidence weights given to the correlation coefficients. The use of genetic algorithms shows that theoretically neutral transformations of the correlation matrix can surprisingly lead to significant sensitivities of the SCR (up to 6%). This highlights the need for a very strong internal control around the PSDization step.
Subjects: 
solvency II
risk aggregation
positive semi-definite
Rebonato-Jäckel
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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